{"id":"W4311138016","doi":"10.1016/j.patter.2022.100655","title":"Early prediction and longitudinal modeling of preeclampsia from multiomics","year":2022,"lang":"en","type":"article","venue":"Patterns","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Stanford Maternal and Child Health Research Institute; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of General Medical Sciences; School of Medicine, Stanford University; Chan Zuckerberg Initiative; Bill and Melinda Gates Foundation; Stanford University; National Institutes of Health; March of Dimes Foundation; Burroughs Wellcome Fund; Foundation for the National Institutes of Health","keywords":"Preeclampsia; Pregnancy; Receiver operating characteristic; Cohort; Confidence interval; Medicine; Population; Univariate analysis; Area under the curve; Univariate; Cohort study; Internal medicine; Bioinformatics; Obstetrics; Biology; Multivariate analysis; Computer science; Machine learning; Multivariate statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000835669,0.00008272511,0.0001817221,0.00004854689,0.0001161509,0.000004738115,0.00004837651,0.00002734444,0.00008830253],"category_scores_gemma":[0.00001333743,0.00008205886,0.00004458897,0.00004871745,0.00002365072,0.00005250024,0.0001453632,0.000165406,0.000001110984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002709849,"about_ca_system_score_gemma":0.00001446679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007246669,"about_ca_topic_score_gemma":0.00002575968,"domain_scores_codex":[0.9993027,0.00003237861,0.0001784229,0.0001898808,0.0001839075,0.0001126949],"domain_scores_gemma":[0.9996819,0.00003637163,0.00005216515,0.0001655671,0.00002464878,0.00003933294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001266185,0.00006001561,0.9947958,0.00005535332,0.0001053811,0.000009114382,0.002279258,0.0005637782,0.0006305411,0.00002676634,0.00002350809,0.001323888],"study_design_scores_gemma":[0.001191231,0.0002371785,0.9313204,0.0001237012,0.000137683,0.00001887653,0.0004455019,0.06577519,0.0002733028,0.0003411226,0.00005477172,0.0000810369],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915473,0.001112768,0.006159966,0.0001250386,0.000139244,0.0001840645,0.0003201331,0.00003686242,0.0003745788],"genre_scores_gemma":[0.9992118,0.0001343427,0.0003730516,0.00004819669,0.00007478045,0.00004477851,0.00005648125,0.00001124751,0.00004526039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06521141,"threshold_uncertainty_score":0.3346263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03745634720873904,"score_gpt":0.2576849203022694,"score_spread":0.2202285730935304,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}